An Improved Pulmonary Nodule Detection Scheme based on Multi-Layered Filtering and 3d Distance Metrics

This paper proposed a computer-aided detection (CAD) system to automatically detect pulmonary nodules from thoracic computed tomography (CT) images. Automatically detect pulmonary nodules is a difficult job because of the large deviation in size, shape, location and density of nodules. The proposed CAD scheme applies multiple 3D disk-shaped laplacian filters to enhance the shape of spherical regions. Optimal multiple thresholding and 3D distance mapping is used to extract regions of interest and separate nodules. Finally, rule-based pruning removes easily dismissible false positive structures. The proposed system provides an overall nodule detection rate of 80% with an average of 12.2 false positives per scan. The experimental results reveals that the proposed CAD can attain a comparatively high performance.

@article{Jahangir2016,
  title     = {An Improved Pulmonary Nodule Detection Scheme based on Multi-Layered Filtering and 3d Distance Metrics},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {8},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Baber Jahangir and Muhammad Imran and Qamar Abbas and Shahina Rabeel1line and Ayyaz Hussain},
  doi       = {10.14569/IJACSA.2016.070839},
  url       = {https://doi.org/10.14569/IJACSA.2016.070839}
}